Detection of Prenatal Cardiac Disease using Computer Vision and Artificial Intelligence

In this era of rapid development of global science and technology, artificial intelligence and industrial technology are converging at an unprecedented speed, driving social progress and industrial upgrading. In order to deeply discuss the latest development in these two and relevant fields, share research results, and promote academic exchanges and cooperation, we are honored to have organized the 2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2024). This proceedings is the crystallization of this event, which brings together the wisdom and insights of experts, scholars, engineers and technicians from all over the world. AIITA 2024 was grandly held in Guangzhou, China from April 12th to 14th, 2024 via hybrid form, attracting over 200 participants from all over the world. The conference aimed to provide a platform for experts and scholars, engineers and technicians, researchers and developers in the fields of artificial intelligence and industrial engineering to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, and strengthen academic research and discussion. During the conference, the participants had an in-depth discussion on a number of topics related to the application of artificial intelligence and industrial technology, and jointly explored the new direction of future development. In the keynote speech part, latest research results and cutting-edge technologies in related fields including Graph Neural Networks: Models and Applications in Pattern Recognition (by Prof. Bo Jiang, Anhui University, China), Distributed GPU-accelerated Evolutionary Computation (by Dr. Ran Cheng, Southern University of Science and Technology, China), Quantum Model Learning (by Prof. Daowen Qiu, Sun Yat-sen University, China), Face Image Editing from the Perspective of Conditional Generative (by Prof. Si Wu, South China University of Technology, China), Cooperative Global Robust Output Regulation for Nonlinear Multi-Agent Systems with Switching Networks (by Prof. Wei Liu, South China University of Technology, China) were discussed. At the same time, we have set up a number of presentations and panel discussion sessions, in which participants gained more opportunities to have in-depth discussions and exchanges on specific issues. This conference calls for papers on a wide range of topics, covering all aspects of artificial intelligence and industrial technology applications. We received hundreds of submissions from all over the world, and after strict evaluation and screening, we finally included some excellent papers in this proceedings. These papers not only have high academic value and practical significance, but also represent the latest research dynamics and trends in the fields of artificial intelligence and industrial technology applications. Finally, we would like to thank all the experts, scholars, engineers and staff who participated in the AIITA 2024 for their support and contribution. We believe that with the joint efforts of all of us, the research and application in the fields of artificial intelligence and industrial technology applications will achieve more brilliant achievements. The Committee of AIITA 2024 List of Committee Member is available in this Pdf.

This book is a platform for anyone who wishes to explore Artificial Intelligence in the field of agriculture from scratch or broaden their understanding and its uses. This book offers a practical, hands-on exploration of Artificial Intelligence, machine learning, deep Learning, computer vision and Expert system with proper examples to understand. This book also covers the basics of python with example so that any anyone can easily understand and utilize artificial intelligence in agriculture field. This book is divided into two parts wherein first part talks about the artificial intelligence and its impact in the agriculture with all its branches and their basics. The second part of the book is purely implementation of algorithms and use of different libraries of machine learning, deep learning and computer vision to build useful and sightful projects in real time which can be very useful for you to have better understanding of artificial intelligence. After reading this book, the reader will an understanding of what Artificial Intelligence is, where it is applicable, and what are its different branches, which can be useful in different scenarios. The reader will be familiar with the standard workflow for approaching and solving machine-learning problems, and how to address commonly encountered issues. The reader will be able to use Artificial Intelligence to tackle real-world problems ranging from crop health prediction to field surveillance analytics, classification to recognition of species of plants etc.

Artificial intelligence is having an increasingly significant impact on the functioning of international trade, productivity dynamics, and policy processes today. This study examined the role of artificial intelligence applications in international trade and identified research trends in the field using a bibliometric approach. The study analyzed 146 articles published between 1981 and 2025, using the keywords ‘international trade’ and ‘artificial intelligence applications’ in the Web of Science database on 4 September 2025. Subject areas and document types were considered during the search process; publication trends, collaborations between countries and institutions, the most-cited studies, and keyword networks were evaluated using VOSviewer. The findings show that academic output in the fields of artificial intelligence and international trade has increased rapidly, particularly since 2018, with 2024 exhibiting the highest publication volume. It was found that most studies were concentrated in the field of Business Economics. The analysis results clearly reveal that the United States, the United Kingdom, and China are leaders in both publication and citation counts. The original contribution of this study is that, unlike bibliometric studies commonly found in the literature that approach the relationship between ‘artificial intelligence and international trade’ from a general perspective, it addresses the subject from an international trade perspective, focusing on artificial intelligence applications. In this respect, the study provides a more targeted contribution to the field by revealing both the direction of development in the literature and the application areas in which artificial intelligence is concentrated in trade processes.

With the rapid development of the information age, the application of artificial intelligence has gradually expanded to various fields, including image and video identification, speech recognition, fingerprint recognition, face recognition, UAV, robot, weather forecast, etc. Deep learning is a very active direction in the field of artificial intelligence. Recently, the state has introduced policies on promoting the classification of domestic waste, and vigorously advocates the classification of waste, while the traditional disposal method wastes manpower, material resources and time. Therefore, this paper mainly introduces the development of deep learning and the application of Python in deep learning, as well as the study of intelligent garbage classification algorithm based on Python deep learning.

Although the fields of artificial intelligence (AI) and simulation are both concerned with the development and use of models, these fields evolved almost independently until relatively recently. Now, the potential contributions of AI to the development and application of simulation models are being considered more widely. AI techniques can play a variety of roles in the simulation process, including: Knowledge representation in a model Decision making within a simulation Rapid prototyping of models Data analysis of simulator-generated outputs Model modification and maintenance.

This position paper presents a synopsis of the imperative function artificial intelligence (AI) has partaken in software engineering (SE) as well as in software testing. In addition, the paper discusses how graphical user interface (GUI), and event driven software testing can derive benefits from the use of AI techniques. Artificial intelligence has significantly aided the process of the automation of different software process. The employment of AI in software testing is not novel, having played a crucial role in the automation of software testing since its innovation. The usage of AI techniques not only reduces the cost but it also guarantees better quality as well as thorough testing. GUI Testing can be considered as the most challenging area of software testing. Although the results are quite preliminary, but the application of different AI techniques for GUI testing has proven to produce ideal results. Nevertheless, the application of AI techniques in GUI testing, in comparison with software testing, which has procured much assistance by venturing with AI, leaves much to be desired.

The cross-fusion research of artificial intelligence technology and spinal surgery represented by machine learning and neural network model is a new research direction and hot issue in the field of artificial intelligence in recent years. The anatomy and disease symptoms of the spine are complex, and the diagnosis and treatment of spinal surgery require rich clinical experience. However, the distribution of medical resources in China is seriously uneven. How to improve the ability of primary medical services so that the most extensive patient groups can benefitis still an urgent problem to be solved. Artificial intelligence is a technical science that researches and develops theories, methods, technologies, and application systems for simulating, extending and expanding human intelligence. With the advent of the era of big data medical technology, artificial intelligence technology may solve this problem by transforming experts sinking into tech sinking . At present, technologies such as confrontation learning, weakly supervised learning, intensive learning and graph neural networks have become research hotspots in the field of artificial intelligence, and have also played an important role in many fields of clinical medicine. Based on the advantages of deep learning and neural network in disease learning, many spine surgeons combine it with the diagnosis and treatment of cervical spondylosis, low back pain, lumbar degenerative diseases, spinal deformity, spinal tumors, and other spine-related diseases. The rapid location and accurate diagnosis of the disease not only makes it an effective tool for the comprehensive diagnosis of spinal diseases but also provides the basis for the most reasonable treatment options for spinal diseases. In the domestic application of artificial intelligence in the diagnosis and treatment of spinal surgery, it can also solve the problems of difficult diagnosis and complicated treatment of spinal diseases faced by primary doctors, reduce the rate of misdiagnosis and missed diagnosis, and effectively reduce the economic and social burden of spinal diseases. This paper reviews the research progress of artificial intelligence represented by deep learning in the field of diagnosis and treatment of spinal surgery at home and abroad, and the advantages and application prospects of artificial intelligence in the diagnosis and treatment of spinal surgery.

Generation Z, consisting of individuals born between 1997 and 2012, faces new challenges in harnessing technological advancements, particularly in the field of artificial intelligence (AI). Despite AI offering various benefits and significant potential, there are several challenges that need to be addressed to ensure the personal resilience of Generation Z in utilizing it. This research aims to analyze the challenges faced by Generation Z in leveraging AI and its impact on their personal resilience. Through qualitative data collection, the researchers analyze factors influencing the utilization of AI by Generation Z and its effects on aspects of mental well-being, privacy, and social intelligence, particularly in relation to the responsibility and ethics of AI utilization. The findings of this study identify several challenges encountered by Generation Z in the utilization of AI. Firstly, the uncertainty and rapid changes in AI technology can lead to anxiety and the inability to keep up with the latest developments. Secondly, the irresponsible use of AI can have negative effects on the mental well-being of Generation Z, such as excessive dependency or information manipulation. Thirdly, privacy and security concerns can pose serious threats when Generation Z uses AI applications that require access to their personal data. Lastly, the possibility of job displacement by AI can impact the career sustainability of Generation Z.

This paper reviews about application of artificial intelligence in medical image informatics. Additionally, it may enhance therapeutic results and increase the value of medical image analysis in yet-to-be-determined ways. Different techniques of imaging like anatomical (x-ray, MRI, ultrasound) and system generated imaging techniques (microscopy, PACs, SPECT) has been discussed in this paper. Deep learning and machine learning approaches for imaging is discussed at later stage. Deep learning techniques in particular are receiving a lot of attention due to their outstanding efficiency in image-recognition tasks in artificial intelligence (AI). They can more effectively and accurately diagnose patients by performing an automated quantitative assessment of complicated medical image properties. Deep learning, particularly image categorization, is being used more and more in the realm of medical images. Machine learning to find tumor image is discussed for brain tumor. For the collection and storage of image data, the Digital Imaging and Communication in medicine is frequently utilized. How OMERO and DICOM database is used for digital imaging is discussed further.

The field of Artificial Intelligence (AI) has shown skyrocketing trend of growth in the 21st century. The evolution of AI has advanced the development of human society in contemporary times, driven by both theoretical innovations and technological breakthroughs. In this era of rapid advancement in computer science, cybernetics, interdisciplinary sciences, automation, mathematical logic, linguistics, physical education, and sports journalism, AI has transitioned from theory to widespread application, becoming a key technology in modern society. AI is increasingly influencing all aspects of human life, including game and sports training. The rise of intelligent systems has introduced a positive impact through precision technologies that continue to enhance the sports arena. Real-time personalized training plans, player performance analytics, automated scouting and recruitment systems, match predictions, and AI-driven sports are among the many practical applications transforming the world of sports. These technologies are not only viable but also essential in improving efficiency, performance, and audience engagement. This study explores how these diverse AI applications support and elevate modern game and sports training.

The presented article discusses the social impact of ‎innovations in the field of ICT and artificial ‎intelligence on society. The article examines the ‎possibilities of artificial intelligence's impact on the ‎social character of society, analyzes artificial ‎intelligence from its positive and negative aspects, and ‎notes the possibilities of pragmatic use of artificial ‎intelligence against the background of new changes, as ‎well as its real and probable consequences. At the same ‎time, it discusses global and regional challenges related ‎to artificial intelligence and analyzes the main goals of ‎national strategies related to artificial intelligence. ‎Preventive security measures and ethical aspects during ‎the use of artificial intelligence against the background ‎of changes in the field of artificial intelligence in ‎Azerbaijan are also among the main research topics.

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

This study examines the interdisciplinary connections that result from deep learning applications in artificial intelligence (AI) across several fields. A division of machine learning recognized as "deep learning" has proven to be remarkably effective at drawing out intricate patterns and representations from enormous datasets. Deep learning algorithms have made important improvements in fields as well as computer vision, natural language processing, robotics, finance, healthcare, advertising, and more by utilizing deep neural networks. This study’s goal is to evaluate the impact of deep learning in AI across disciplines and to draw attention to its revolutionary potential. The study examines the specific uses of deep learning in several fields and talk about the potential and problems that come with its deployment. The study provides a look at how precise object recognition, scene comprehension, and image production made deep learning transform the computer vision. Deep learning approaches have improved machine translation, sentiment analysis, and dialogue systems in natural language processing. This research study offers insights into the possibility of interdisciplinary cooperation by looking at the uses, difficulties, and future prospects of deep learning in AI. It demonstrates the transformative impact of deep learning in fostering creativity and advancement across a variety of sectors, opening the door for the creation of more intelligent and advanced AI systems.

Deep Learning, a buzz in the artificial intelligence field, is the subset of machine learning. It teaches computers to learn from examples in order to perform a task that is intuitive to humans. It is also known as a deep neural network or deep neural learning. In deep learning, neural networks have a significant role. These are a set of algorithms that we implement to identify relevant relationships in datasets, and they follow the process that imitates the human brain. Neural networks depict the behavior of the human brain and enable computer algorithms to identify trends. It also solves complex problems in the domain of machine learning, AI, and data science.Deep learning deploys artificial neural networks to recognize the hidden patterns of data in the dataset provided. These algorithms are trained over an adequate amount of time and applied to a data set.

Editorial: "Cyber Security Prevention, Defenses Driven by AI and Mathematical Modelling and Simulation Tools". The rise of the Smart Computation driven by the Artificial Intelligence with the ubiquitous access to Data Computer Communication Technologies and Internet by anyone, from anywhere at any time, has become a working, learning and socializing platform for people worldwide and if often known as Cyberspace. The past century technological advancements in the Data Communications Technologies , Semiconductor Industry (SI) and a Very Large Scale Integration (VLSI) contributed to significant increase of computer systems performance and computational capacity of small portable computers and cell phones that have become available at low cost for people anywhere in the world. Due to relatively low cost, anyone and anywhere in the world may purchase a PC, Laptop, Smart Phone, or Tablet. These smart-computational devices are capable of powerful computation and connecting almost any computer system in the world via Internet 24/7. Given the current unstable geopolitical ecosystem in conjunction with the dynamic development in the artificial intelligence (AI), smart computation and fast Internet with the ubiquitous access to Internet 24/7, the reliable and secure provision of confidentiality, integrity and accessibility of data available via Internet, has become essential for government, business, the industry and individual worldwide. The fast developments in the field of AI gave rise to a new era of smart computation that can easily be utilized by the governments, special groups or very well trained and skilled individual with malicious intend due to their political , economic or personal motivation. Given the rise of next generation of smart cyberattacks, the use of AI and Mathematical Modeling and Simulations Tools has become essential part of cybersecurity tool kit, to make sure that contra cyber measures are most effective and reliable in real-time 24/7. The Research Topic was received with great interest in the scholastic and professional community worldwide. The paper selection was very competitive based on rigorous reviews presenting seven papers. The Research Topic presents a collection of selected peer reviewed papers presenting most recent advances and the state-of-the-art in the field of. Applied AI and Mathematical Modelling in Cyber Security. The papers show evidence that cyberattacks are becoming much more sophisticated, computationally complex, and challenging for the current cyber security mechanisms, which drives the research, innovation, and development of the next generation of AI assisted Cyber Security imbedded with the Mathematical Modelling and Simulations tools. The first paper presents practical application of eXplainable AI (XAI) in cybersecurity while enabling a machine-human interaction. The second paper discusses effective use of AI applied in the cyberattacks prevention utilizing Intrusion Detection and Prevention Systems (IDPS). The third paper presents use of deep learning and machine learning algorithms to identify the malicious malware. The fourth paper discusses the use of encryption and cryptographic algorithms in creating an effective cyber defenses in the Internet of Thing Cyber Infrastructures. The fifth paper presents effective methods to identify possible cyberattacks utilizing the noisy audio evidence. The sixth paper illustrates utilization of simulation based on the dropout mechanisms to identify obfuscated malicious traffic. The seventh paper presents effective applications of Generative AI Influenced Spread, Extended Models and Awareness Spread Models to analyze fake news propagation and enable the intervention strategy assessment. The Research Topic brings to light most recent developments in the field of applied AI and Mathematical Modeling and Simulations in Cyber Security, while presenting current and future challenges presented by ongoing challenges in the field of studies. Given the current dynamic advancements in the field of AI, Smart Computation and AI, we are yet at the very beginning of new era of new generation of ultra-smart and most sophisticated cyberattacks that will require a new ultra-smart cyber defenses to make sure that the future cyberspace will be safe, reliable and beneficial to the betterment of mankind for future generations for many years to come. The Research Topic promotes creation of the AI Inspired Cyber Security Center, bringing together multidisciplinary research teams working together while seeking best solutions and mechanism to make the current and future cyberspace safe, secure and reliable 27/7 in the nation and worldwide. The Topic Editors would like to use this opportunity to express most sincere gratitude to contributors, reviewers, to Dr. Enrique Morillas, Dr. Nicola Zannone, Mr Sean O'Reilly Ms Alice Gooch, and Colleagues at Frontiers in Computer Science for their strong support, kind guidance and good counsel provided during the course of the project.